Fault Diagnosis of Steam Turbine-Generator Sets Using CMAC Neural Network Approach and Portable Diagnosis Apparatus Implementation

Based on the vibration spectrum analysis, this paper proposed a CMAC (Cerebellar Model Articulation Controller) neural network diagnosis technique to diagnose the fault type of turbine-generator sets. This novel fault diagnosis methodology contains an input layer, quantization layer, binary coding layer, excited memory addresses coding unit, and an output layer to indicate the fault type possibility. Firstly, we constructed the configuration of diagnosis scheme depending on the vibration fault patterns. Secondly, the known fault patterns were used to train the neural network. Finally, combined with a Visual C++ program the trained neural network can be used to diagnose the possible fault types of turbine-generator sets. Moreover, a PIC microcontroller based portable diagnosis apparatus is developed to implement the diagnosis scheme. All the diagnosis results demonstrate the following merits are obtained at least: 1) High learning and diagnosis speed. 2) High noise rejection ability. 3) Eliminate the weights interference between different fault type patterns. 4) Memory size is reduced by new excited addresses coding technique. 5) Implement easily by chip design technology.